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Fixes #153790 Pull Request resolved: https://github.com/pytorch/pytorch/pull/154022 Approved by: https://github.com/Skylion007
169 lines
6.5 KiB
Python
169 lines
6.5 KiB
Python
# mypy: ignore-errors
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import torch
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from torch.utils._pytree import tree_map
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from typing import Optional
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from collections.abc import Iterator
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import logging
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import contextlib
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import itertools
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from torch.utils._dtype_abbrs import dtype_abbrs as _dtype_abbrs
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from torch.utils._python_dispatch import TorchDispatchMode
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from torch.utils.weak import WeakTensorKeyDictionary
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import functools
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from torch._C._profiler import gather_traceback, symbolize_tracebacks
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logger = logging.getLogger("LoggingTensor")
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# How the chain of calls works for LoggingTensor:
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# 1. Call torch.sin
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# 2. Attempt __torch_function__. In LoggingTensor torch function is disabled so we bypass it entirely
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# 3. Enter dispatcher, wind your way through Autograd
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# 4. Hit Python dispatch key, call __torch_dispatch__
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# This Tensor can work with autograd in two ways:
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# - The wrapped Tensor does not require gradients. In that case, the LoggingTensor
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# can require gradients if the user asks for it as a constructor kwarg.
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# - The wrapped Tensor can require gradients. In that case autograd will be tracked
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# for the wrapped Tensor and the LoggingTensor itself cannot require gradients.
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# WARNING: We allow these two possibilities for testing purposes. You should NEVER use both in a single
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# test or you might get surprising behavior.
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# TODO: TensorBase should work
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class LoggingTensor(torch.Tensor):
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elem: torch.Tensor
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__slots__ = ['elem']
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context = contextlib.nullcontext
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@staticmethod
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def __new__(cls, elem, *args, **kwargs):
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# The wrapping tensor (LoggingTensor) shouldn't hold any
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# memory for the class in question, but it should still
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# advertise the same device as before
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r = torch.Tensor._make_wrapper_subclass(
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cls, elem.size(),
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strides=elem.stride(), storage_offset=elem.storage_offset(),
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# TODO: clone storage aliasing
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dtype=elem.dtype, layout=elem.layout,
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device=elem.device, requires_grad=kwargs.get("requires_grad", False)
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)
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# ...the real tensor is held as an element on the tensor.
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r.elem = elem.detach() if r.requires_grad else elem
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return r
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def __repr__(self):
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return super().__repr__(tensor_contents=f"{self.elem}")
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@classmethod
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def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
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def unwrap(e):
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return e.elem if isinstance(e, cls) else e
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def wrap(e):
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return cls(e) if isinstance(e, torch.Tensor) else e
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with cls.context():
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rs = tree_map(wrap, func(*tree_map(unwrap, args), **tree_map(unwrap, kwargs)))
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logging.getLogger("LoggingTensor").info(f"{func.__module__}.{func.__name__}", args, kwargs, rs) # noqa: G004
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return rs
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class LoggingTensorMode(TorchDispatchMode):
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def __torch_dispatch__(self, func, types, args=(), kwargs=None):
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if kwargs is None:
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kwargs = {}
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rs = func(*args, **kwargs)
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logging.getLogger("LoggingTensor").info(f"{func.__module__}.{func.__name__}", args, kwargs, rs) # noqa: G004
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return rs
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class LoggingTensorReentrant(LoggingTensor):
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context = torch.overrides.enable_reentrant_dispatch
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# https://stackoverflow.com/questions/36408496/python-logging-handler-to-append-to-list
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class LoggingTensorHandler(logging.Handler):
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def __init__(
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self, log_list: list[str], use_shortid_for_all_tensors: bool,
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with_type: bool, tracebacks_list: Optional[list]) -> None:
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logging.Handler.__init__(self)
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self.log_list = log_list
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self.use_shortid_for_all_tensors = use_shortid_for_all_tensors
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self.tracebacks_list = tracebacks_list
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self.memo = WeakTensorKeyDictionary()
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self.next_id = 0
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self.with_type = with_type
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def _shortid(self, t: torch.Tensor) -> int:
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if t not in self.memo:
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self.memo[t] = self.next_id
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self.next_id += 1
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return self.memo[t]
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def _fmt(self, a: object, with_type: bool = False) -> str:
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cond_cls = torch.Tensor if self.use_shortid_for_all_tensors else LoggingTensor
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if isinstance(a, cond_cls):
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maybe_type = ""
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if with_type and self.with_type:
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maybe_type = f": {_dtype_abbrs[a.dtype]}[{', '.join(map(str, a.shape))}]"
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x = f"${self._shortid(a)}{maybe_type}"
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return x
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else:
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return repr(a)
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def emit(self, record):
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fmt_args = ", ".join(
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itertools.chain(
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(str(tree_map(self._fmt, a)) for a in record.args[0]),
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(f"{k}={str(tree_map(self._fmt, v))}" for k, v in record.args[1].items()),
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)
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)
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fmt_rets = tree_map(functools.partial(self._fmt, with_type=True), record.args[2])
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self.log_list.append(f'{fmt_rets} = {record.msg}({fmt_args})')
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if self.tracebacks_list is not None:
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self.tracebacks_list.append(record.traceback)
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def log_input(name: str, var: object) -> None:
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logger.info("input", (name,), {}, var) # noqa: PLE1205
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class GatherTraceback(logging.Filter):
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def __init__(self, python=True, script=True, cpp=False):
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self.python = python
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self.script = script
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self.cpp = cpp
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def filter(self, record):
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record.traceback = gather_traceback(python=self.python, script=self.script, cpp=self.cpp)
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return True
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@contextlib.contextmanager
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def capture_logs(is_mode=False, python_tb=False, script_tb=False, cpp_tb=False) -> Iterator[list[str]]:
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collect_traceback = python_tb or script_tb or cpp_tb
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log_list: list[str] = []
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tracebacks_list: list[str] = []
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handler = LoggingTensorHandler(
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log_list,
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with_type=True,
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use_shortid_for_all_tensors=is_mode,
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tracebacks_list=tracebacks_list if collect_traceback else None
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)
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logger.addHandler(handler)
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logger.setLevel(logging.INFO)
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logger.propagate = False
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if collect_traceback:
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logger.addFilter(GatherTraceback(python=python_tb, script=script_tb, cpp=cpp_tb))
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try:
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if collect_traceback:
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yield log_list, tracebacks_list
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else:
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yield log_list
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finally:
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symbolized_tracebacks = symbolize_tracebacks(tracebacks_list)
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tracebacks_list.clear()
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tracebacks_list.extend(symbolized_tracebacks)
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logger.removeHandler(handler)
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@contextlib.contextmanager
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def capture_logs_with_logging_tensor_mode(python_tb=False, script_tb=False, cpp_tb=False):
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with LoggingTensorMode(), capture_logs(True, python_tb, script_tb, cpp_tb) as logs:
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yield logs
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